CamouflageCamouflage is the use of any combination of materials, coloration, or illumination for concealment, either by making animals or objects hard to see, or by disguising them as something else. Examples include the leopard's spotted coat, the battledress of a modern soldier, and the leaf-mimic katydid's wings. A third approach, motion dazzle, confuses the observer with a conspicuous pattern, making the object visible but momentarily harder to locate, as well as making general aiming easier.
Active camouflageActive camouflage or adaptive camouflage is camouflage that adapts, often rapidly, to the surroundings of an object such as an animal or military vehicle. In theory, active camouflage could provide perfect concealment from visual detection. Active camouflage is used in several groups of animals, including reptiles on land, and cephalopod molluscs and flatfish in the sea. Animals achieve active camouflage both by color change and (among marine animals such as squid) by counter-illumination, with the use of bioluminescence.
Artificial neural networkArtificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.
Motion camouflageMotion camouflage is camouflage which provides a degree of concealment for a moving object, given that motion makes objects easy to detect however well their coloration matches their background or breaks up their outlines. The principal form of motion camouflage, and the type generally meant by the term, involves an attacker's mimicking the optic flow of the background as seen by its target.
Ship camouflageShip camouflage is a form of military deception in which a ship is painted in one or more colors in order to obscure or confuse an enemy's visual observation. Several types of marine camouflage have been used or prototyped: blending or crypsis, in which a paint scheme attempts to hide a ship from view; deception, in which a ship is made to look smaller or, as with the Q-ships, to mimic merchantmen; and dazzle, a chaotic paint scheme which tries to confuse any estimate of distance, direction, or heading.
Military camouflageMilitary camouflage is the use of camouflage by an armed force to protect personnel and equipment from observation by enemy forces. In practice, this means applying colour and materials to military equipment of all kinds, including vehicles, ships, aircraft, gun positions and battledress, either to conceal it from observation (crypsis), or to make it appear as something else (mimicry). The French slang word camouflage came into common English usage during World War I when the concept of visual deception developed into an essential part of modern military tactics.
Salience (neuroscience)Salience (also called saliency) is that property by which some thing stands out. Salient events are an attentional mechanism by which organisms learn and survive; those organisms can focus their limited perceptual and cognitive resources on the pertinent (that is, salient) subset of the sensory data available to them. Saliency typically arises from contrasts between items and their neighborhood. They might be represented, for example, by a red dot surrounded by white dots, or by a flickering message indicator of an answering machine, or a loud noise in an otherwise quiet environment.
Generative adversarial networkA generative adversarial network (GAN) is a class of machine learning framework and a prominent framework for approaching generative AI. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. Given a training set, this technique learns to generate new data with the same statistics as the training set.
CountershadingCountershading, or Thayer's law, is a method of camouflage in which an animal's coloration is darker on the top or upper side and lighter on the underside of the body. This pattern is found in many species of mammals, reptiles, birds, fish, and insects, both in predators and in prey. When light falls from above on a uniformly coloured three-dimensional object such as a sphere, it makes the upper side appear lighter and the underside darker, grading from one to the other.
Deep learningDeep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Adversarial machine learningAdversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems in industrial applications. To understand, note that most machine learning techniques are mostly designed to work on specific problem sets, under the assumption that the training and test data are generated from the same statistical distribution (IID).
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Large language modelA large language model (LLM) is a language model characterized by its large size. Their size is enabled by AI accelerators, which are able to process vast amounts of text data, mostly scraped from the Internet. The artificial neural networks which are built can contain from tens of millions and up to billions of weights and are (pre-)trained using self-supervised learning and semi-supervised learning. Transformer architecture contributed to faster training.
Similarity measureIn statistics and related fields, a similarity measure or similarity function or similarity metric is a real-valued function that quantifies the similarity between two objects. Although no single definition of a similarity exists, usually such measures are in some sense the inverse of distance metrics: they take on large values for similar objects and either zero or a negative value for very dissimilar objects. Though, in more broad terms, a similarity function may also satisfy metric axioms.
CrypsisIn ecology, crypsis is the ability of an animal or a plant to avoid observation or detection by other animals. It may be a predation strategy or an antipredator adaptation. Methods include camouflage, nocturnality, subterranean lifestyle and mimicry. Crypsis can involve visual, olfactory (with pheromones) or auditory concealment. When it is visual, the term cryptic coloration, effectively a synonym for animal camouflage, is sometimes used, but many different methods of camouflage are employed by animals or plants.
Neural architecture searchNeural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks that are on par or outperform hand-designed architectures. Methods for NAS can be categorized according to the search space, search strategy and performance estimation strategy used: The search space defines the type(s) of ANN that can be designed and optimized. The search strategy defines the approach used to explore the search space.
AttentionAttention is the concentration of awareness on some phenomenon to the exclusion of other stimuli. It is a process of selectively concentrating on a discrete aspect of information, whether considered subjective or objective. William James (1890) wrote that "Attention is the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought. Focalization, concentration, of consciousness are of its essence.
Learning to rankLearning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Training data consists of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment (e.g. "relevant" or "not relevant") for each item.
Measurement uncertaintyIn metrology, measurement uncertainty is the expression of the statistical dispersion of the values attributed to a measured quantity. All measurements are subject to uncertainty and a measurement result is complete only when it is accompanied by a statement of the associated uncertainty, such as the standard deviation. By international agreement, this uncertainty has a probabilistic basis and reflects incomplete knowledge of the quantity value. It is a non-negative parameter.
Edge detectionEdge detection includes a variety of mathematical methods that aim at identifying edges, curves in a at which the image brightness changes sharply or, more formally, has discontinuities. The same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in , machine vision and computer vision, particularly in the areas of feature detection and feature extraction.
